R: Predict method for GLMMs fitted with MCMCglmm

predict.MCMCglmm {MCMCglmm} | R Documentation

## Predict method for GLMMs fitted with MCMCglmm

### Description

Predicted values for GLMMs fitted with MCMCglmm

### Usage

    ## S3 method for class 'MCMCglmm'
    predict(object, newdata=NULL, marginal=object$Random$formula,
            type="response", interval="none", level=0.95, it=NULL,
            posterior="all", verbose=FALSE, approx="numerical", ...)

### Arguments

`object` | an object of class `"MCMCglmm"`
`newdata` | An optional data frame in which to look for variables with which to predict
`marginal` | formula defining random effects to be maginalised
`type` | character; either "terms" (link scale) or "response" (data scale)
`interval` | character; either "none", "confidence" or "prediction"
`level` | A numeric scalar in the interval (0,1) giving the target probability content of the intervals.
`it` | integer; optional, MCMC iteration on which predictions should be based
`posterior` | character; if `it` is `NULL` should marginal posterior predictions be calculated ("all"), or should they be made conditional on the marginal posterior means ("mean") of the parameters, the posterior modes ("mode"), or a random draw from the posterior ("distribution").
`verbose` | logical; if `TRUE`, warnings are issued with newdata when the original model has fixed effects that do not appear in newdata and/or newdata has random effects not present in the original model.
`approx` | character; for distributions for which the mean cannot be calculated analytically what approximation should be used: numerical integration (`numerical`; slow), second order Taylor expansion (`taylor2`) and for logistic models approximations presented in Diggle (2004) (`diggle`) and McCulloch and Searle (2001) (`mcculloch`)
`...` | Further arguments to be passed

### Value

Expectation and credible interval

### Author(s)

Jarrod Hadfield j.hadfield@ed.ac.uk

### References

Diggle P, et al. (2004). Analysis of Longitudinal Data. 2nd Edition. Oxford University Press.

McCulloch CE and Searle SR (2001). Generalized, Linear and Mixed Models. John Wiley & Sons, New York.

### See Also

`MCMCglmm`

[Package MCMCglmm version 2.30 Index]